medRxiv preprint doi: https://doi.org/10.1101/2020.09.12.20193219; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Sero-surveillance (IgG) of SARS-CoV-2 among Asymptomatic General population of Paschim Medinipur, West Bengal, India
(Conducted during last week of July and 1st week of August 2020) - A Joint Venture of VRDL Lab (ICMR), Midnapore Medical College & Hospital & Department of Health and Family Welfare, Paschim Medinipur
Satpati PS1*, Sarangi SS2*, Gantait KS3#, Endow S4#, Mandal NC5#, Kundu Panchanan6#, Bhunia Subhadip7#, Sarangi Soham8# Abstract:-
Background: Coronavirus disease 2019 (COVID-19) has emerged as a pandemic, and the infection due to SARS- CoV-2 has now spread to more than 200 countries 3. Surveillance systems form the foundation stone of active case finding, testing and contact tracing, which are the key components of the public health response to this novel, emerging infectious disease 4. There is uncertainty about the true proportion of patients who remain asymptomatic or pre-symptomatic at a given time. As per the WHO-China Joint Monitoring Mission Report, and an analysis of 21 published reports, anywhere between 5 and 80 per cent of SARS-CoV-2-infected patients have been noted to be asymptomatic 5, 6 Whereas in India 4197563 cases are positive, in which in West Bengal total 180788 cases (4.04% of Cases of India) positive of COVID 19. In Paschim Medinipur (West Medinipur) district contributing total 5489 cases (3.03% cases of West Bengal) 9 , 10,11. In this scenario, we want to know the status of IgG seroprevalence of SARS-CoV-2 among asymptomatic general population, so that we can determine the extent of infection of SARS-CoV-2 in general population.
Objectives:- Primary Objective:- To estimate the seroprevalence for SARS-CoV-2 infection in the general asymptomatic population at Paschim Medinipur District. Secondary Objectives- To estimate age and sex specific seroprevalence. To determine the socio demographic risk factors for SARS-CoV-2 infection; To determine the other risk factors like comorbidities, vaccination status, travel history, contact history etc.; To determine the durability of Immunity (IgG) conferred by natural infection of SARS-CoV-2 in individuals previously RTPCR positive.
Methodology: It was a cross sectional 30 cluster study among the population of Paschim Medinipur district of West Bengal conducted in last week of July and 1st week of August 2020 among 458 asymptomatic general population and 30 RTPCR positive cases in 30 villages or wards of municipalities. 30 clusters were chosen from list of COVID 19 affected villages/wards of municipality as per PPS (Probability Proportional to Size) method.
Results: Of the 458 asymptomatic general population,19 asymptomatic people found to be seropositive IgG for SARS-CoV-2 with Mean or average total seropositivity rate of 4.15%. 19 Out of 30 (63.33%) RTPCR positive patients found Seronegative. Median of Days between RTPCR test and sero negativity found was 60 with minimum 28 days to maximum 101 days and Range of 73 days and a standard deviation of 19.46. Among risk factors, the risk of having IgG is more in persons having Travel history with odds ratio of 2.99- 95%CI (1.17- 7.65) with p-value-0.02. Hydroxychloroquine prophylaxis with Odds ratio of 8.49- 95% CI(1.59-45.19) with p value - 0.003. Occupation as migrant labour with Odds ratio of 5.08- 95% CI(1.96-13.18) with p value of 0.001. H/O Chicken pox with Odds ratio of 2.15- 95% CI(0.59-7.79) with p value of 0.017. Influenza vaccinated with Odds ratio of 8.07 with 95% CI (0.8-81.48) with a p value of 0.036.
Conclusion: Of the 458 asymptomatic general population,19 asymptomatic people found to be seropositive IgG for SARS-CoV-2 with Mean or average total seropositivity rate of 4.15%. 19 Out of 30 (63.33%) RTPCR positive patients found Seronegative. Median of Days between RTPCR test and sero negativity found was 60 with minimum 28 days to maximum 101 days and Range of 73 days and a standard deviation of 19.46. Those having Travel History and having occupation as Migrant Labourer – have significantly higher probability of getting infected with SARS-CoV-2. No role has been found of Hydroxychloroquine Medicines as Chemoprophylactic. No durable immunity conferred by natural infection with SARS-CoV-2 –mean time to become seronegative after positive RTPCR test 60 days. So there is a chance of reinfection after average 2 months.
1-MD, Head of the Department, Dept. of Microbiology, Midnapore Medical College and Hospital, Medinipur, Paschim Medinipur, WB, INDIA; 2- DPH, MPH, Deputy Chief Medical Officer of Health- I, Paschim Medinipur District, WB, INDIA;3- MD, Professor, Department of Medicine, Midnapore Medical College and Hospital, Medinipur, Paschim Medinipur, WB, INDIA;4-MD, Assistant Professor, Department of Microbiology, Midnapore Medical College and Hospital, Medinipur, Paschim Medinipur, WB, INDIA;5-Chief Medical Officer of Health, Paschim Medinipur District, WB, INDIA;6-MS, Principal, Midnapore Medical College and Hospital, Medinipur, Paschim Medinipur, WB, INDIA; 7-DPH, SMO, NPSP Unit Kharagpur, WHO-India ; 8-2nd Professional MBBS, Medical College, Kolkata, WB, INDIA
*- Principal Investigator; contributed equally #- Co-Investigator
NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. medRxiv preprint doi: https://doi.org/10.1101/2020.09.12.20193219; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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INTRODUCTION: Serosurveys are studies that test body fluids, most commonly blood but also oral fluid, to estimate what proportion of the population has been vaccinated against or previously infected with a pathogen—and how many people remain susceptible 1. Conducting population-based serosurveillance for severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) will estimate and monitor the trend of infection in the adult general population, determine the socio-demographic risk factors and delineate the geographical spread of the infection 2. Coronavirus disease 2019 (COVID-19) has emerged as a pandemic, and the infection due to SARS- CoV-2 has now spread to more than 200 countries 3. As on 6th September 2020, the Confirmed cases in India stood at 4197563, West Bengal had 180788 confirmed cases, and Paschim Medinipur District had 5489 confirmed cases.9,10,11 Surveillance systems form the foundation stone of active case finding, testing and contact tracing, which are the key components of the public health response to this novel, emerging infectious disease 4. There is uncertainty about the true proportion of patients who remain asymptomatic or pre-symptomatic at a given time. As per the WHO-China Joint Monitoring Mission Report, and an analysis of 21 published reports, anywhere between 5 and 80 per cent of SARS-CoV-2- infected patients have been noted to be asymptomatic 5, 6 The WHO global research map for COVID-19 and others recommend population-level sero epidemiological studies to generate data on the levels of infection in populations and recommend containment measures accordingly7. In order to achieve this, a protocol for conducting such population- based sero-epidemiological investigation for COVID-19 has been proposed by the WHO 8. Serosurveys involve collection of specimens to measure the presence and level of antigen-specific antibodies in a group of people. It reveals what proportion of the population has been exposed to an infectious disease or has been vaccinated against a pathogen. It also estimates the level of population immunity to one or more infectious diseases; Sero Survey identifies gaps in immunity because people were not vaccinated or previously infected. Immunity gaps can be in specific age groups, certain locations, or among specific populations such as migrants. Identifying immunity gaps can guide immunization programs. It estimates parameters for modelling and transmission dynamics to measure disease burden and guide immunization programs 12. Serologic studies are crucial for clarifying dynamics of the coronavirus disease pandemic. Past work on serological studies (e.g., during influenza pandemics) has made relevant contributions. Although detection of antibodies to measure exposure, immunity, or both seems straightforward conceptually, numerous challenges exist in terms of sample collection, what the presence of antibodies means, and appropriate analysis and interpretation to account for test accuracy and sampling biases. Successful deployment of serologic studies depends on type and performance of serologic tests, population studies, use of adequate study designs, and appropriate analysis and interpretation of data. We highlight key questions that serologic studies can help answer at different times, review strengths and limitations of different assay types and study designs, and discuss methods for rapid sharing and analysis of serologic data to determine the extent of transmission of SARS-CoV-2. SARS-CoV-2 serologic studies has largely focused on 2 questions: first, what proportion of a population has been infected; and second, what proportion of a population is immune to disease or infection? Firstly, for infections that elicit detectable antibody responses, serologic studies can detect past infection regardless of clinical symptoms. This capability is useful for understanding the extent of past transmission13. Secondly, if measured antibody responses correlate with protection, serologic studies can be used to measure the proportion of the population those who are immune. This information can be used to guide control policies, help identify populations that are still susceptible to epidemics, target treatment or vaccination trials, and target vaccination when available. Although much discussion around use of serologic testing to inform persons of their serologic status has occurred, crucial distinctions exist between the use of serologic information to estimate population-level versus person-level immunity. Person-level immunity information is currently fraught with scientific, ethical, and legal uncertainties, which we do not address in this article.
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OBJECTIVES:- Primary Objective:- To estimate the seroprevalence for SARS-CoV-2 infection in the general asymptomatic population at District level. Secondary Objectives:- (i)To estimate age and sex specific seroprevalence; (ii)To determine the socio demographic risk factors for SARS-CoV-2 infection; (iii) To determine the other risk factors like comorbidities, vaccination status, travel history, contact history etc; (iv) To determine the durability of Immunity (IgG) conferred by natural infection of SARS-CoV-2 in individuals previously RTPCR positive. METHODOLOGY Study design—Cross sectional 30 Cluster study Study Population- • Total Population of Paschim Medinipur District-52,40,571 • Total Number of affected Block/Municipality- 28 • Total Number of CoVID 19 affected villages/wards- 219 For case definitions:- We have followed the WHO1 and ICMR2 Protocols for Serosurvey Sampling method-30 cluster study- 30 clusters were chosen from list of COVID 19 affected villages/wards of municipality as per PPS (Proportionate to population size) method. Total Sample Size- 450 as calculated by StatCalc of Epi-info 7. So, 15 samples per 30 clusters were tested. Finally, 458 Samples were collected from the selected 30 Clusters. Along with these, 30 Samples of previously RT-PCR positive individuals were collected. Ethical Clearance was taken from Medinipur Medical College Ethical Clearance Committee. Method of data collection- 30 (thirty) , two membered team comprising of concerned PHN and BPHN, MT (Lab) detailed from same block visited the selected village and PHN/BPHN collected data as per pre-tested Semi structured schedule and MT drew blood and after making serum, transport it as per protocol maintaining cold chain to VRDL Lab of MMCH. Lab Test- IgG ELISA was done using ErbaLisa COVID-19 IgG ELISA Kit [Manufactured by Calbiotech Inc., USA (Erba-TransAsia Group Company)] having a Sensitivity of 98.3% and Specificity of 98.1%. Data Analysis- Data Analysis was done by SPSS 27 and Epi Info 7. For Sociodemographic Variables and Risk Factors, comparison done between IgG positive and IgG Negative Groups- Odds Ratio calculated (95% Interval), Chi-Square value, degree of freedom and p-value (<0.05) calculated and factors found with strong association and significant p value put in Multinomial Logistic Regression Analysis.
RESULTS : - A 30 cluster cross-sectional study conducted in the last week of July and 1st week of August 2020 among 458 asymptomatic general population and 30 RTPCR positive cases in 30 villages or wards of municipalities. Descriptive Epidemiology of the Asymptomatic General Population (Table 1) :-Among the samples collected, 62.7% were Males (n=287), and 37.3% were Females (n=171). The highest no. of samples were from 20-45 years of age group (63.3%; n=290). 63.1% persons were from Rural Area (n=289), 30.3% persons were from Urban Area (n=139). 6.6% of the persons lived in a Periurban Area (n=30). 7.9% Samples were from Slum Area (n=36). 20.5% Persons had History of Travel(n=94) [Interstate- 14.4%; International-1.5%; Interdistrict- 4.6%]. 79.5% Persons had no History of Travel. 42.1% of the medRxiv preprint doi: https://doi.org/10.1101/2020.09.12.20193219; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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persons had Contact History with a Covid-19 Patient (n=193) [Close and Direct Contact-7.2%; Indirect Contact- 32.8%; Healthcare Provider- 2.2%]. IgG Serosurvey Results of Pachim Medinipur District among Asymptomatic General Population of Paschim Medinipur District (Table 2);- 19 asymptomatic persons found to be seropositive IgG for SARS-CoV-2 with Mean or average total seropositivity rate of 4.15% [95% Confidence Interval- Lower-2.52% Upper-6.40%]. 439 asymptomatic general persons were found to be Seronegative(95.85%). Highest Seropositivity percentage found in Ghatal Municipality of 12.50% followed by Daspur II of 9.78%, Daspur I of 4.00% and Kharagpur Municipality of 3.70% and Midnapur Municipality of 6.25%. Socio-demographic Factors associated with Increased Risk of IgG Seropostivity of COVD-19 (Table-3) were- Male Sex – with odds ratio of 2.3 (95% conf. Int.-0.75
Occupation as Migrant Labour was significant with Significance of 0.038 and Exp.(B) of 8.007 Hydroxychloroquine Intake was significant with Significance of 0.045 and Exp.(B) of 7.290 IgG Serosurvey Results of Pachim Medinipur District among Previously RT-PCR Positive of Paschim Medinipur District(Table 7, Table 8):- Out of 30 Samples Collected, only 11 previously RT-PCR Positive Individuals were found IgG Seropositive for COVID-19 (36.67%). 19 Persons were found Seronegative (63.33%)[Table 7]. The Mean and Median no. of Days Between RTPCR test and IgG Test in IgG Negative Persons were 60.32 and 60 respectively with a minimum of 28 days and Maximum of 101 Days. (Table 8). Whereas, in the case of IgG Positive Individuals, the Mean and Median No. of Days between RTPCR and IgG ELISA Test were 48.09 and 49 respectively with a minimum of 31 days and Maximum of 67 days (Table 8). DISCUSSION:- The findings of the IgG Serosurvey conducted during last week of July to first week of August indicated that 4.15 percent of the asymptomatic General Population of Paschim Medinipur District, West Bengal, India were exposed to SARS-CoV-2 infection. The seroprevalence ranged from 12.50% in Ghatal medRxiv preprint doi: https://doi.org/10.1101/2020.09.12.20193219; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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Municipality to 0.00 % in Garhbeta-I, Dantan-I, Dantan-II, Chandrakona-I, Chandrakona Municipality, Ghatal Block, Pingla and Keshpur blocks of the district. As per the Nationwide Serosurvey conducted by ICMR in May-June 202033, the nationwide seroprevalence was 0.73 percent. Though our seroprevalence was higher than the national average, it was much lower than the 22.86% seroprevalence in Delhi. Since the seroprevalence in our district is still low, it indicates that a majority of the population of our district was still susceptible to COVID-19 infection. It also correlates with the study done in England in which they have found around 4.2% sero positivity in the month of May 2020 15. A dashboard of sero-epidemiological data available from 22 countries estimated the pooled seroprevalence to be 4.76 per cent, ranging from 0.65 percent in Scotland to 26.6 per cent in Iran36. Other studies also correlate the same prevalence in the young population in different countries 20,21,22,23,24,25 In this study in 20 to 45 yr age group we have found that 3.79% IgG positivity which is around same percentage which in the seroprevalence study of Switzerland 14 found in week 1 around first week of April 2020 found (around 3.5%). Seropositivity was higher among Males (5.23%). This is also seen in the ICMR Serosurvey33, and other studies around the globe.20,21,22,23,24,25. In India, Migrant Labours have been regarded as a high risk group for SARS-CoV-2 infection. This is also reflected by the fact that being a Migrant Labour was significantly associated with IgG Seropositivity with Odds Ratio of 5.07 (95% conf. Interval- 1.96
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after positive RTPCR test 60 days. So, there is a chance of reinfection after 2 months. Those who were vaccinated by influenza vaccine previously and H/O chicken pox are more prone to become IgG Seropositive of SARS COV2 among asymptomatic healthy populations. Acknowledgements:- We acknowledge the contributions and support of all staffs and officers of Department of Health and Family Welfare, Paschim Medinipur and all scientists, staffs, Laboratory Technicians, Data Entry Operators engaged in VRDL Lab (ICMR), Midnapore Medical College and Hospital, Paschim Medinipur. Financial Support and Sponsorship:- Financial Support provided by District Health & Welfare Samiti of Paschim Medinipur District. Conflict of Interest:- None
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Table 1:- Descriptive Epidemiology:-
Sl.no. Characteristics Sub-Category Frequency Percentage
MALE 287 62.7 1 SEX FEMALE 171 37.3 0-5 yrs 1 .2 11-19 yrs 28 6.1 2 AGE 20-45 yrs 290 63.3 46-59 yrs 101 22.1 60 yrs and above 38 8.3 RURAL 289 63.1 AREA OF 3 URBAN 139 30.3 RESIDENCE PeriUrban 30 6.6 NO 422 92.1 4 SLUM AREA YES 36 7.9 INTERSTATE 66 14.4 INTERNATIONAL 7 1.5 5 TRAVEL HISTORY INTERDISTRICT 21 4.6 NO TRAVEL 364 79.5 HISTORY CLOSE AND DIRECT 33 7.2 CONTACT INDIRECT CONTACT 150 32.8 6 CONTACT HISTORY HEALTHCARE 10 2.2 PROVIDER NO SUCH 265 57.9 HISTORY
Table 2: SARS-CoV-2 SERO-SURVEILLANCE RESULTS IN GENERAL POPULATION OF PASCHIM MEDINIPUR
BLOCK Total no. of samples No. of No. of IgG Seropositivity collected for IgG Negative Percentage Serosurveillance Positive (95% CI) Midnapore Municipality 16 1 15 6.25(0.16-30.23) Ghatal Municipality 16 2 14 12.50(1.55-38.35) Garhbeta I Block 15 0 15 0.00 Chandrakona Municipality 15 0 15 0.00 Dantan II 15 0 15 0.00 Dantan I 14 0 14 0.00 KGP Municipality 108 4 104 3.70(1.02-9.21) Daspur I 75 3 72 4.00(0.83-11.25) Daspur II 92 9 83 9.78(4.57-17.76) Ghatal Block 30 0 30 0.00 Chandrakona I 15 0 15 0.00 Pingla 16 0 16 0.00 Keshpur 31 0 31 0.00 TOTAL 458 19 439 4.15 (2.52-6.40)
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Table 3: SOCIO-DEMOGRAPHIC FACTORS
Sl. CHARACTERIS SUB-CATEGORY IgG Negative IgG Positive Odds 95% Conf. Interval of chi- p—value No TICS n(%) n(%) Ratio Odds Ratio square (Significant for . p<0.05) Lower Upper
1 SEX MALE (n=287) 272(94.77%) 15(5.23%) 2.3 0.75 7.05 2.24 0.13
FEMALE (n=171) 167(97.66%) 4(2.34%)
2 AGE-GROUP 0-5 yrs (n=1) 1(100.00%) 0(0.00%)
11-19 yrs (n=28) 25(89.29%) 3(10.71%)
20-45 yrs (n=290) 279(96.21%) 11(3.79%) 3.02 0.82 11.11 3.022 0.082
46-59 yrs (n=101) 97(96.04%) 4(3.96%)
60 yrs & above 37(97.37%) 1(2.63%) (n=38) 3 LOCALITY RURAL(n=289) 275(95.16%) 14(4.84%) 1.67 0.59 4.72 0.95 0.33
URBAN(n=139) 134(96.40%) 5(3.60%) 1.23 0.43 3.48 0.15 0.696
PeriUrban(n=30) 30(100.00%) 0(0.00%)
4 SLUM AREA NO (n=422) 406(96.21%) 16(3.79%)
YES (n=36) 33(91.67%) 3(8.33%) 2.30 0.64 8.32 1.72 0.19
5 EMPLOYMEN MIGRANT 55(87.30%) 8(12.70%) 5.07 1.96 13.18 13.42 0.00 T LABOUR (n=63) GOVT EMPLOYEE 45(95.74%) 2(4.26%) (n=47) PRIVATE 18(94.74%) 1(5.26%) EMPLOYEE (n=19) HEALTHCARE 17(94.44%) 1(5.56%) PROVIDER (n=18)
AGRICULTURE 53(100.00%) 0(0.00%) (n=53) BUSINESS (n=43) 42(97.67%) 1(2.33%)
OTHERS (n=168) 163(97.02%) 5(2.98%)
UNEMPLOYED 46(97.87%) 1(2.13%) (n=47) 6 Educational ILLITERATE 39(97.5%) 1(2.5%) Qualification (n=40) PRE-PRIMARY 16(100.00%) 0(0.00%) (n=16) PRIMARY (n=163) 157(96.32%) 6(3.68%) 0.82 0.31 2.22 0.14 0.71
SECONDARY 93(93.00%) 7(7.00%) (n=100) HIGHER 51(98.08%) 1(1.92%) SECONDARY (n=52) GRADUATION 61(95.31%) 3(4.69%) (n=64) POST 22(95.65%) 1(4.35%) GRADUATION (n=23) 7 Monthly Income UPTO RS1000 91(97.85%) 2(2.15%) (n=93) 1001-3000 (n=43) 43(100.00%) 0(0.00%)
3001-5000 (n=103) 98(95.15%) 5(4.85%) 1.24 0.44 3.53 0.17 0.68
5001-10000 (n=116) 110(94.83%) 6(5.17%) 1.38 0.51 3.72 0.41 0.52
10001-25000 (n=50) 46(92.00%) 4(8.00%)
25001-50000 (n=32) 31(96.88%) 1(3.12%)
ABOVE 50000 20(95.24%) 1(4.76%) (n=21)
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Table 4: Comorbidity Profile SL. COMORBID SUB- IgG IgG Odds 95% chi— df p—value NO ITIES CATE Negative Positive Ratio Confidence square (Significant GORY n(%) n(%) Limit for for p<0.05) Odd's Ratio Upper Lower
1 DIABETES NO 400 18 (n=418) (95.69%) (4.31%) YES 39 1 0.57 0.07 4.38 0.299 1 0.584 (n=40) (97.5%) (2.5%) 2 DIABETES NO 407 18 MEDICATIO (n=425) (95.76%) (4.24%) N TAKEN YES 32 1 0.71 0.03 4.08 0.11 1 0.74 (n=33) (96.97%) (3.03%) 3 BLOOD MORE 50 1 0.43 0.05 3.3 0.69 1 0.405 SUGAR THAN (98.04%) (1.96%) (RANDOM) 140 (IN mg/dl) (n=51) LESS 389 18 THAN (95.58%) (4.42%) 140 (n=407) 4 HYPERTEN NO 387 18 SION (n=405) (95.56%) (4.44%) YES 52 1 0.41 0.05 3.16 0.77 1 0.38 (n=53) (98.11%) (1.89%) 5 ANTI- NO 399 18 HYPERTEN (n=417) (95.68%) (4.31%) SIVE DRUGS YES 40 1 0.55 0.07 4.26 0.33 1 0.57 TAKEN (n=41) (97.56%) (2.44%)
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Table 5:- Summary of Risk FACTORS for SEROPOSITIVITY
95% chi— p— Confidence square value Limit for value (Signifi Odd's Ratio cant for SL. RISK SUB- IgG Negative IgG Odds U Lo p<0.05) NO FACTORS CATEGORY n(%) Positive Ratio p wer df n(%) p e r 1 TRAVEL NO TRAVEL 353(96.98%) 11 HISTORY HISTORY (3.02%) (n=364) TRAVEL 86(91.49%) 8(8.51%) 2.99 1. 7.65 5.66 1 0.02 HISTORY 17 PRESENT (n=94) 2 HYDROXYCH NO(n=450) 433(96.22%) 17(3.78%) LOROQUINE YES (n=8) 6(75.0%) 2(25.0%) 8.49 1. 45.19 8.9 1 0.003 PROPHYLAXIS 59 3 MIGRANT NOT A 384(97.21%) 11(2.78%) LABOURER MIGRANT (n=395) MIGRANT 55(87.30%) 8(12.70%) 5.08 1. 13.18 13.43 1 0.001 LABOURER 96 (n=63) 4 HISTORY OF NO (n=270) 264(97.78%) 6(2.22%) CHICKEN POX UNKNOWN 93(91.18%) 9(8.82%) (n=102) YES (n=86) 82(95.34%) 4(4.65%) 2.15 0. 7.79 8.18 2 0.017 59 5 INFLUENZA None (n=235) 228(97.02%) 7(2.98%) VACCINATION Unknown 208(94.98%) 11(5.02%) STATUS (n=219) Yes (n=4) 3(75.0%) 1(25.0%) 8.07 0. 81.48 4.41 1 0.036 8
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Table 6: Multinomial Logistic Regression Table
Model Fitting Information Model Fitting Criteria Likelihood Ratio Tests Model Chi- -2 Log Likelihood df Sig. Square
Intercept Only 58.030 Final 37.494 20.535 7 .005
Parameter Estimates
IgG Status OR Covid 19 Seropositivity B Std. Wald df Sig. Exp 95% Error (B) Confidence Interval for Exp(B) Low Upper er Bound Bou nd POSITIV Intercept - 1.614 2.232 1 .135 E 2.41 2 [occupation_code=.00] 2.08 1.003 4.306 1 .038 8.00 1.12 57.131 (Migrant Labourer) 0 2 [occupation_code=1.00] 0b 0 (Not a Migrant Labourer) [HYDROXYCHLOROQU 1.98 .992 4.008 1 .045 7.29 1.04 50.971 INE_ TAKEN=0] (Yes) 6 3 [HYDROXYCHLOROQUI 0b 0 NE_TAKEN=1] (No) [Travel_History_recode=0] .785 1.019 .594 1 .441 2.19 .297 16.164 (No Travel History) [Travel_History_recode=1] 0b 0 (Travel History Present) [chickenpox_hist_final=0] - .776 .331 1 .565 .640 .140 2.931 (No) .447 [chickenpox_hist_final=1] .868 .915 .900 1 .343 2.38 .396 14.315 (Unknown) [chickenpox_hist_final=2] 0b 0 (Yes) [influenza_vaccn_final=0] - 1.468 1.787 1 .181 .141 .008 2.495 (No) 1.96 2 [influenza_vaccn_final=1] - 1.530 1.916 1 .166 .120 .006 2.414 (Unknown) 2.11 8 [influenza_vaccn_final=2] 0b 0 (Yes)
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Table 7: SERO-SURVEY RESULTS AMONG PREVIOUSLY COVID-19 RTPCR POSITIVE PATIENTS IN PASCHIM MEDINIPUR
Total no. of samples No. of IgG No. of IgG Seropositivity BLOCK collected for Positive Negative Percentage Serosurveillance Ghatal Municipality 1 0 1 0.00 Garhbeta I Block 1 0 1 0.00 Chandrakona Municipality 1 0 1 0.00 Dantan II 1 0 1 0.00 KGP Municipality 7 4 3 57.14 Daspur I 8 2 6 25.00 Daspur II 8 5 3 62.50 Ghatal Block 1 0 1 0.00 Chandrakona I 1 0 1 0.00 Keshpur 1 0 1 0.00 TOTAL 30 11 19 36.67
Table 8: RELATIONSHIP OF “DAYS BETWEEN IgG ELISA TEST & RTPCR TEST” AND IgG SERO- STATUS
Statistics Days between IgG and rtPCR IgG NEGATIVE N Valid 19 Missing 0 Mean 60.32 Std. Error of Mean 4.464 Median 60.00 Mode 49a
Std. Deviation 19.460 Range 73 Minimum 28 Maximum 101 IgG POSITIVE N Valid 11 Missing 0 Mean 48.09 Std. Error of Mean 4.216 Median 49.00 Mode 31a
Std. Deviation 13.982 Range 36 Minimum 31 Maximum 67 a. Multiple modes exist. The smallest value is shown medRxiv preprint doi: https://doi.org/10.1101/2020.09.12.20193219; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
REFERENCES:-
1. World Health Organization’s UNITY studies protocol: Population-based age-stratified seroepidemiological investigation protocol for coronavirus 2019 (COVID-19) infection 2. Muthusamy Santhosh Kumar TB, et al. National sero-surveillance to monitor the trend of SARS-CoV-2 infection transmission in India: Protocol for community-based surveillance. Indian Journal of Medical Research. 2020;151(5):419-23. 3. World Health Organization. Coronavirus disease 2019 (COVID-19) situation report – 90. Geneva: WHO; 2020. 4. World Health Organization. Global surveillance for COVID-19 caused by human infection with COVID- 19 virus: Interim guidance, 20 March 2020. Geneva: WHO; 2020. 5. World Health Organization. Report of the WHO-China joint mission on coronavirus disease 2019 (COVID-19); 16-24 February 2020. Geneva: WHO; 2020. 6. Heneghan C, Brassey J, Jefferson T. COVID-19: What proportion are asymptomatic? The Centre for Evidence-Based Medicine; 6 April, 2020. Available from: https://www.cebm.net/covid-19/covid-19- what-proportion-are-asymptomatic/, accessed on April 24, 2020 7. World Health Organization. Coordinated global research roadmap: 2019 novel coronavirus; March 2020. Geneva: WHO; 2020 8. World Health Organization. Population-based age-stratified seroepidemiological investigation protocol for COVID-19 virus infection. Available from: https://www.who.int/publications-detail/population- based-age-stratified-seroepidemiological-investigation-protocol-for-covid-19-virus-infection, accessed on April 24, 2020. 9. Ministry of Health and Family Welfare, Govt. of India https://www.mohfw.gov.in/ 10. COVID-19 India https://www.covid19india.org/state/WB 11. West Bengal Department of Health and Family Welfare, COVID-19 Daily Bulletins https://www.wbhealth.gov.in/pages/corona/bulletin 12. Kempen JH, Abashawl A, Kinfemichael H, Difabachew MN, Kempen CJ, Debele MT, et al. SARS CoV- 2 Serosurvey in Addis Ababa, Ethiopia. medRxiv. 2020. 13. Organization WH. Population-based age-stratified seroepidemiological investigation protocol for COVID-19 virus infection. Geneva, Switzerland2020. 14. Silvia Stringhini AW, Giovanni Piumatti, Andrew S Azman, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. The Lancet. 2020. 15. Public Health England GoU. Sero-surveillance of COVID-19 2020 [Available from: https://www.gov.uk/government/publications/national-covid-19-surveillance-reports/sero-surveillance- of-covid-19. 16. Martinez de Salazar P, Gomez-Barroso D, Pampaka D, Gil JM, Penalver B, Fernandez-Escobar C, et al. Lockdown measures and relative changes in the age-specific incidence of SARS-CoV-2 in Spain. medRxiv. 2020. 17. Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020; 382: 727–33. 18. Lin H Chen, MD, David O Freedman, MD, Leo G Visser, MD, PhD, COVID-19 Immunity Passport to Ease Travel Restrictions?, Journal of Travel Medicine, Volume 27, Issue 5, July 2020, taaa085, https://doi.org/10.1093/jtm/taaa085 19. WHO. International Health Regulations (2005), Third Edition 2016. Available at: https://www.who.int/ihr/publications/9789241580496/en/. Last accessed May 21, 2020. 20. Instituto de Salud Carlos III. COVID-19 in Spain. https://cnecovid. isciii.es/covid19 (accessed July 2, 2020). 21. European Centre for Disease Prevention and Control. COVID-19 situation update worldwide, as of 18 June 2020. https://www.ecdc. europa.eu/en/geographical-distribution-2019-ncov-cases (accessed July 2, 2020). 22. Sood N, Simon P, Ebner P, et al. Seroprevalence of SARS-CoV-2- specific antibodies among adults in Los Angeles County, California, on April 10–11, 2020. JAMA 2020; published online May 18. https://doi.org/10.1001/jama.2020.8279. 23. Valenti L, Bergna A, Pelusi S, et al. SARS-CoV-2 seroprevalence trends in healthy blood donors during the COVID-19 Milan outbreak. medRxiv 2020; published online May 31. https://doi.org/ 10.1101/2020.05.11.20098442 (preprint). 24. Snoeck CJ, Vaillant M, Abdelrahman T, et al. Prevalence of SARS-CoV-2 infection in the Luxembourgish population: the CON-VINCE study. medRxiv 2020; published online May 18. https://doi.org/10.1101/2020.05.11.20092916 (preprint). 25. Stringhini S, Wisniak A, Piumatti G, et al. Seroprevalence of anti-SARS-CoV-2 IgG antibodies in Geneva, Switzerland (SEROCoV-POP): a population-based study. Lancet 2020. DOI:10.1016/s0140- 6736(20)31304-0. 26. Karimi A, Rafiei Tabatabaei S, Khalili M, Sadr S, Alibeik M, et al. COVID-19 and Chickenpox as a Viral Co-Infection in a 12-Year-Old Patient, a Case Report, Arch Pediatr Infect Dis. Online ahead of Print ; 8(3):e105591. doi: 10.5812/pedinfect.105591 medRxiv preprint doi: https://doi.org/10.1101/2020.09.12.20193219; this version posted September 14, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY-NC-ND 4.0 International license .
27. Elsaie, ML, Youssef, EA, Nada, HA. Herpes zoster might be an indicator for latent COVID 19 infection. Dermatologic Therapy. 2020; 33:e13666. https://doi.org/10.1111/dth.13666 28. Marzano AV, Genovese G, Fabbrocini G, et al. Varicella-like exanthem as a specific COVID-19- associated skin manifestation: Multicenter case series of 22 patients. J Am Acad Dermatol. 2020;83(1):280-285. doi:10.1016/j.jaad.2020.04.044 29. Long QX, Liu BZ, Deng HJ, et al. Antibody responses to SARS-CoV-2 in patients with COVID-19. Nat Med. 2020;26(6):845-848. doi:10.1038/s41591-020-0897-1 30. Deeks JJ, Dinnes J, Takwoingi Y, et al. Antibody tests for identification of current and past infection with SARS-CoV-2. Cochrane Database Syst Rev. 2020;6(6):CD013652. Published 2020 Jun 25. doi:10.1002/14651858.CD013652 31. Song SK, Lee DH, Nam JH, Kim KT, Do JS, Kang DW, Kim SG, Cho MR. IgG Seroprevalence of COVID-19 among Individuals without a History of the Coronavirus Disease Infection in Daegu, Korea. J Korean Med Sci. 2020 Jul;35(29):e269. https://doi.org/10.3346/jkms.2020.35.e269 32. Duaa W. Al-Sadeq, Gheyath K. Nasrallah,The incidence of the novel coronavirus SARS-CoV-2 among asymptomatic patients: A systematic review,International Journal of Infectious Diseases,Volume 98,2020,Pages 372-380,ISSN 1201-9712, https://doi.org/10.1016/j.ijid.2020.06.098. 33. Murhekar Manoj V. et. al. Prevalence of SARS-CoV-2 infection in India: Findings from the national serosurvey, May-June 2020. Indian Journal of Medical Research. Ahead of Print 10Sept2020, 10.4103/ijmr.IJMR_3290_20 34. Boulware DR. Hydroxychloroquine as Postexposure Prophylaxis for Covid-19. New England Journal of Medicine. 2020;383(11):1087–9. 35. Mahase E. Covid-19: Hydroxychloroquine was ineffective as postexposure prophylaxis, study finds. BMJ. 2020;:m2242. 36. SeroTracker. Prevalence of antibodies against SARS-CoV-2infection. Available from: https://serotracker.com/Dashboard, accessed on September 10, 2020. 37. Barman SR. Sero finding: Not all who recovered from Covid had antibodies. The Indian Express [Internet]. [cited 2020Sep12]; Available from: https://indianexpress.com/article/cities/delhi/sero- finding-not-all-who-recovered-from-covid-had-antibodies-6578030/